ECO102H1S: Principles of Macroeconomics Practice Midterm 2, Winter 2024 1) Consider the basic planned aggregate expenditure model below: a. Solve for short-run equilibrium output in terms of exogenous variables and parameters. b. What is the effect of a decrease in planned investment on short-run equilibrium output? Your answer should include specific variables from the PAE model. c. What is the effect of an increase in taxes on short-run equilibrium output? Your answer should include specific variables from the PAE model. d. Suppose the government is required to always save 20% more than it spends, so T = 1.2G. With this new condition, re-solve the model for short-run equilibrium output. e. Following part d, what is the effect of an increase in government spending on short-run equilibrium output? Is this more or less than the effect without the government saving condition from part d? Does this make sense? 2) For the following questions, refer to the Keynesian cross diagram below. a. Assume the economy begins in short-run equilibrium. Suppose that the government starts to spend heavily in order to improve the nation’s public infrastructure. Which PAE line would the economy start and end up at? b. Assuming prices are fixed in the short-run, what would happen immediately to actual investment compared to planned investment? What would be the resulting level of actual output? c. Suppose that in the long-run, the government’s efforts are successful: the amount and quality of public infrastructure has increased. Where would the economy end up? d. Now suppose the opposite occurs: the government’s extra spending on infrastructure is wasted and does little to improve the nation’s capital stock. If the government maintains its higher spending, what would be the effect on prices in the long-run? Why? 3) Suppose planned aggregate expenditure is given by the following values: a. Solve for short-run equilibrium output as a function of autonomous spending and the real interest rate. b. What is the value of the aggregate expenditure multiplier? Explain why there is a multiplier and how it works. c. Suppose the real interest rate is currently 7%. If potential output is 21, what is the value of the output gap? Is this a recessionary gap or expansionary gap? d. Currently the economy is in a period of high uncertainty. By the next period, the economy begins to settle, causing the risk premium to fall by 2.5 percentage points. What is the new level of the output gap? e. By how much would the government need to change government spending to return the economy to its long-run equilibrium? 4) For each of the following shocks, assume that the economy starts in long-run equilibrium, and expected inflation stays constant. Specify the type of shock and which curve(s) in the IS-MP-PC model shift (in words, no need to draw). Determine the immediate effect on the output gap , the real interest rate r and unexpected inflation. a. Business confidence increases unexpectedly. b. The sudden collapse of a bank in Silicon Valley increases the fear of a nationwide banking crisis. c. A judge unexpectedly agrees with labor unions: all transportation workers get a 10% wage increase. 5) For the following questions use the Phillips curve described below: a. Suppose inflation in the economy is currently 7%, but households only believe it will be 5% next year. If there are no shocks affecting the economy, what must be the size of the output gap? b. Draw a graph of the Phillips curve, labeling the axes as well as the origin point of your graph. What is the slope of the Phillips curve? c. Next year households adjust their expectations upward, now believing inflation will be 7%. Depict how this will affect the Phillips curve graph. Clearly indicate how the curve has changed.
Applied Project, 200 MASY1-GC | 4100 | Spring 2025 Course Description Where academically warranted and subject to departmental approval, the student will undertake and deliver a real-world project for active practitioners in the field. Under the direction of a faculty supervisor, the student will select and explore a topic to be developed. The student will then be required to generate a practical application of his/her findings that demonstrates professional level mastery of the subject matter involved. The project will be reviewed by the faculty supervisor and one other expert in the field. Notes | Prerequisites Prerequisite: MASY 1200, MASY 1210, MASY 1220, MASY 1230, MASY 1240, MASY 1250, MASY 1260, MASY 3310 Learning Outcomes At the conclusion of this course, students will be able to: 1. Design a project proposal that will be used to initiate, conduct, and complete an Applied Project (completed within the initial 6-week remote course timeframe). 2. Design a situational analysis (completed by Week 10). 3. Generate a literature survey (completed by Week 1). 4. Design a project charter and associated work breakdown structure (WBS), risk analysis and communications plan (completed within the initial 6-week synchronous course timeframe). 5. Select a Project Sponsor who is a management-level individual at the organization benefitting from the Applied Project. The Project Sponsor must have the authority toinsert_line_breakapprove the student's Project Proposal and any required resources and be available throughout the project to act as the student's mentor for the project. The Projectinsert_line_breakSponsor will also be asked to approve in writing the student's work at key intervals throughout the course and the ensuing project and be available for at least one meetinginsert_line_break(virtual or inperson) with the course professor. 6. Design a project goal and a set of project objectives that will include metrics for demonstrating project success (completed within the initial 6-week remote course timeframe). 7. Conduct an Applied Project while operating in the dual role of (a) Project Manager employing the Project Management Body of Knowledge methodology and (b) Projectinsert_line_breakImplementer, successfully meeting the project goal and objectives all within the timeframe. set forth in the project plan section of the project proposal. (This will extendinsert_line_breakbeyond the initial 6-week remote course timeframe). 8. Write a post-project final report describing the project, its planning phase, and its implementation phase
COMP3340 Applied Deep Learning The University of Hong KongAssignment 1Feb 2025Question 1 - XOR ApproximationWe consider the problem of designing a feedforward neural network to approximate the XORfunction. Specifically, for any input points (x1, x2), x1, x2 ∈ {0, 1}, the output of the networkshould be approximately equal to x1 ⊕ x2. Suppose the network has two input neurons, onehidden layer with two neurons, and an output layer with one neuron, as shown in Figure 1.The activation function for all neurons is the Sigmoid function defined as σ(z) = 1+1e−z .(a) Please provide the specific values for the parameters in your designed network. Demon strate how your network approximates the XOR function (Table 1) by performing forwardpropagation on the given inputs (x1, x2), x1, x2 ∈ {0, 1}.(b) If we need the neural network to approximate the XNOR function (Table 1), how shouldwe modify the output neuron without altering the neurons in the hidden layer?x1 x2 x1 ⊕ x2 x1 x20 0 0 10 1 1 01 0 1 01 1 0 1Table 1: XOR and XNOR Value Table ! " #Figure 1: Network structure and the notation of parametersCOMP3340 Applied Deep Learning The University of Hong KongQuestion 2 - BackpropagationWe consider the problem of the forward pass and backpropagation in a neural network whosestructure is shown in Figure 1. The network parameters is initialized as w1 = 1, w2 = −2,w3 = 2, w4 = −1, w5 = 1, w6 = 1, b1 = b2 = b3 = 0. The activation function for all neuronsis the Sigmoid function defined as σ(z) = 1+1e−z .(a) Suppose the input sample is (1, 2) and the ground truth label is 0.1. Please computethe output y of the network.(b) Suppose we use the Mean Squared Error (MSE) loss. Please compute the loss value forthe sample (1, 2) and its gradient with respect to the network parameters using chain rules.The final answer should be limited to 3 significant figures.(c) Suppose we use stochastic gradient descent (SGD) with a learning rate of α = 0.1.Please specify the parameters of the network after one step of gradient descent, using thegradient computed in (b). Please also specify the prediction value and the corresponding lossof the new network on the same input (1, 2).
SOSC1449 Understanding Our Economy Assignment 1 Due: 17 March 2023 23:59 1. Demand and supply: Gloria’s Demand Schedule Price Quantity Demanded 8 1 7 4 6 6 5 8 4 10 3 12 2 14 1 16 Taylor’s Demand Schedule Price Quantity Demanded 5 2 4 4 3 6 2 8 1 10 A. Given the two students’ demand schedules for bubble tea, plot the individual demand curve for each student on a Price-Quantity figure. [5 points] B. Plot the aggregate demand curve for the two students. [5 points] C. If the market price is 4.2, how many bubble teas are bought by each of the students? [5 points] D. Compute the consumer surplus when the market price is 3.5. [5 points] 2. The labor market for college graduates can exhibit four possible scenarios based on changes in demand and supply: (1) A profession hires more college graduates, and their wage increases. (2) A profession hires more college graduates, but their wage decreases. (3) A profession hires fewer college graduates, and their wage increases. (4) A profession hires fewer college graduates, and their wage decreases. For each scenario, start with an initial supply and demand diagram for the labor market (with wage on the vertical axis and quantity of college graduates on the horizontal axis). Then, shift either the demand curve, the supply curve, or both to illustrate how the market reaches the described outcome. Label the initial and new equilibrium points clearly. [20 points] 3. Suppose an economy produces only computers and coffees. In Year 1, it produces 150 computers at a price of $20 each and 100 coffees at $10 each. In Year 2, it produces 200 computers at a price of $200 each and 200 coffees at $100 each. Using Year 1 as the base year, calculate the percentage increase from Year 1 to Year 2 for the following variables: a. Nominal GDP [4 points] b. Real GDP [4 points] c. CPI (base year quantity weights) [4 points] d. GDP deflator (final year quantity weights) [4 points] In Year 3, the economy produces 400 computers at a price of $250 each and 200 coffees at $150 each. Using Year 1 as the base year for real GDP and CPI, calculate the percentage increase from Year 2 to Year 3 for the following variables: e. Nominal GDP [4 points] f. Real GDP [4 points] g. CPI (base year quantity weights) [4 points] h. GDP deflator (final year quantity weights) [4 points] 4. Growth model: 1. Describe, using a mathematical equation, how capital stock accumulates over time in an economy. [2 points] 2. Identify the two main forces driving the accumulation of capital over time. [2 points] 3. On a graph with output (Y) on the vertical axis and capital stock (K) on the horizontal axis, plot the production function and saving function. [2 points] 4. On the same graph, with output (Y) on the vertical axis and capital stock (K) on the horizontal axis, add the depreciation function. [2 points] 5. Using the same Y-K graph, illustrate the dynamics of catching-up growth, showing how capital stock evolves over time (starting from a very low capital and a very high capital), and mark the steady-state equilibrium. [2 points] 6. Derive the function Kt+1=f(Kt) that represents the capital accumulation process depicted in the graph shown below. [2 points] 7. Suppose a country starts with a capital stock per worker, K0, below its steady- state level. Using the graph above, describe how the country’s capital stock grows over time. [2 points] 8. For the country in question 7, explain how the growth rate of capital per worker changes as it approaches the steady state. [2 points] 9. After reaching the steady state, does output per worker exhibit permanent growth? One line to explain why or why not. [2 points] 10. Describe the effects on output (Y) and capital stock (K) if a policy increases the savings rate (s) in the economy, using the Solow model framework. [2 points] 11. Describe the effects on output (Y) and capital stock (K) if a policy increases total factor productivity (A) in the economy, using the Solow model framework. [2 points] Suppose that when capital per worker is below a subsistence level (K< Kbar), there is no saving because all income is consumed for survival. When capital per worker exceeds this level (K> Kbar), saving occurs normally. 12. Rewrite the capital accumulation equation mathematically for the case where KKbar, [2 points] 14. Illustrate the capital accumulation dynamics for both cases (K< Kbar and K>Kbar) using the graph below. [2 points]
HOMEWORK 4, NANO203 DUE March 14, 5 pm 1. (20 points) You are working on alloyed plasmonic nanoparticles, focusing on making smaller nanoparticles to increase the total surface area for increased sensitivity and activity in sensing and catalysis, respectively. a. Identify and describe a characterization approach (technique and expected contrast/analysis mechanism) to assess the composition and structure of colloidal Cu-Au alloy nanoparticles (
BUS2108 - GLOBAL BUSINESS ENVIRONMENT Individual Project – International Business Ethics Semester 2, 2024/25 – Section 1 Project Title: Ethical issues in international business: An Analysis of Corporate Operations, Policies and Disclosure Alignment with Learning Goals: This assignment is intended to contribute to the business Programme Learning Goal 7, “Students are able to demonstrate effective consideration of ethical issues in business situations” Required: You required to research, evaluate, and reflect upon the ethical dimensions of the international operations of a firm that produces something you have bought (e.g. laptop, t-shirt, food product). In your answer you should consider and discuss the following questions: Research I. Introduce the specific product you bought – what is it made of and where do the components of the product typically originate from? II. How easily does the firm make it for you, the consumer, to find information related to the brand's suppliers? How does the brand claim to monitor and assess components of its supply chain? III. Identify, from external sources, issues related to the firm’s operations which may raise ethical issues or concerns. Focus on the ethical issues covered in class (working conditions, human rights, corruption, environmental concerns) but you can also note other ethical issues if they exist. These issues can be for the firm’s overall operations and need not specifically be about your particular product. Evaluate IV. Does the company have its own internal code of conduct or does it adhere to any industry-wide initiatives governing ethical operations? Give evidence from the code of conduct that indicates whether or not the firm is satisfactorily addressing the ethical issues raised in III above? V. Do the policies of the firm reflect a ‘universal’ or a ‘relativist’ ethical approach by the firm? VI. What recommendations would you make or what other actions could the firm take to improve its ethical behaviour? Reflect VII. With the information you were able to find, would you consider the brand to be ethical? VIII. Did you consider ethical issues before you purchased the product? would you continue to purchase from this same firm/brand in future? Sources of information and choice of product You can utilise information provided by the firm (websites/ annual reports etc.) in your answers; however, you should NOT RELY ONLY on information from the firm. Try to make use of your internet search skills to review accreditation bodies, uncover any criticisms of the firm, or look for issues related to suppliers / countries / industries involved in their supply chains. You can pick absolutely any product that you have bought from any company. The only exceptions to this are you CANNOT pick products made by either STARBUCKS, UNIQLO, NIKE or APPLE (there is too much, too easily available, information and published resources available on these firms). Length and format of Report Your report should be approx 1,500 words in length excluding Appendices. Please attach copies of relevant news clippings, ethical codes of conduct etc. as necessary. Please also attach a photo of yourself together with the product you have chosen for the project. As far as possible the report should be written in the format of a conventional business report, rather than that of an essay. This means dividing the report into sections with appropriate section titles and numbering. Date and Place of Submission Submit a Turnitin version of your report before 17:00 on Saturday March 15th. Penalties will be assigned for late submission. No e-mailed submissions will be accepted. Marking and Return of Reports The projects will be marked in accordance with the marking rubric attached to this assignment. Check your finished report against the marking rubric to make sure that you have fulfilled all the criteria of the assignment. Reports will be marked and returned to you as promptly as possible. Plagiarism and referencing issues This is an individual assignment. You should not discuss it with your classmates and the work submitted must be all your own. Selected assignments may be analysed using the Turnitin anti-plagiarism software. Please refer to and abide by the course policy regarding the use of A.I., software will also check whether or not you have utilized A.I. in your essay. When making reference to external sources, please make use of in-text references or use of appropriate footnotes, instead of just a listing of sources at the end of your submission. Questions Your lecturer will be pleased to answer any specific questions you may have about this assignment. By e-mail, please make an appointment to speak with your lecturer during his/her assigned office hours, providing an indication of when you would like to meet with the lecturer and a summary of the questions you would like to ask.
DGN4210 3D modelling & virtual space Learning Outcomes • Be able to use 3D imaging software to create environments and objects • Have an understanding of the principles of designing objects and spaces in three-dimensions • Be able to apply surface qualities, lighting, textures, and imported graphic files to elements in a three dimensional composition • Be capable of exploring the range of options and controls • available through 3D imaging and modelling software applications • Understand and manipulate camera properties and viewer perspectives • Have knowledge of the various tools and techniques used for realtime 3D computer graphic applications. Details of Task During the first half of the semester, you will receive three sets of images that you need to use as a reference to create accurate models of objects. To assist you in improving your skills in Polygon Extrusion and NURBS modelling, you will participate in class tutorials. Once you have completed the tutorials, you can apply this knowledge to the exercises and challenges like those covered in class. You must ensure that your models of the objects, which include chess pieces, a bottle, and a computer mouse, match the reference images provided by your lecturer. Any work that does not meet this requirement will not be accepted. Make sure to create your models using real-world scale and explore surreal arrangements. Note that you can only scale the objects in the final submission. To pass Project 1, you need to complete three exercises - Exercise 1, Exercise 2, and Exercise 3. Exercise 1 - 10% • Model the King, Rook, and Pawn chess pieces accurately to the image given to you by the lecturer. • Define the Maya project folder to provide an organized Maya folder structure. Ensure you do not move the project definition file “workspace.mel.” • The Maya project file must contain the well-labelled parts of the chess pieces. Parts detached in the real world must be detached in 3D, labelled and grouped accordingly during the modelled process. • Submit on Moodle a zipped Maya project folder that has your name and student ID as the folder name. • Due date end of week 3 - Saturday by 11:55 PM Exercise 2 - 10% • Model the bottle based on reference images provided in week 2. (Due end of week 5) • Define the Maya project folder to provide an organized Maya folder structure. • The Maya project file must contain well-labelled parts of the bottle. Parts detached in the real world must be detached, labelled and grouped accordingly during the modelled process. • Submit on Moodle a zipped Maya project folder that has your name and student ID as the folder name. Include the project definition file named “workspace.mel” in the Maya project folder. • Due date end of week 5 - Saturday by 11:55 PM Exercise 3 - 10% 1. Model the computer mouse based on the reference images provided by the lecturer. If the model is not close to the reference image(s) the work will not be accepted. 2. Import objects from Exercises 1 and 2, arrange them in a visually pleasing composition and apply 3-point lighting and basic shaders. Rename the objects with descriptive labels. 3. Scale the images to real-world sizes and model them. 4. Arrange the 3D objects in a visually appealing composition, considering camera angles and 3D space dynamics. Allow for surreal arrangements and scaling as per your artistic vision. 5. Consolidate all three exercises into a single Maya file, labelling various parts of objects and grouping them with well-labels for clarity. Do not combine the objects; the lecturer must see the various parts. You will get close to nothing if you combine objects with various parts. 6. Apply basic Arnold shaders to the objects to enhance their visual appeal. 7. Set up basic 3-point lighting to illuminate the scene effectively. 8. Create 3d cameras with different lenses to provide the work with interesting points of view that you can render. 9. Submit a well-defined Maya project file on Moodle with reference images and three rendered images in their appropriate subfolder - “sourceImages” and “images”, respectively. Remove any unnecessary files and folders from the project folder before submitting. 10. The project is due end of week 7 on Saturday by 11:55 PM Deliverables Submit on Moodle a zipped Maya project folder that has your name and student ID as the folder name. The Maya project file must contain all three exercises, well-labelled and grouped objects. Include the project definition file named “workspace.mel” in the Maya project folder. Ensure the reference materials are in the appropriate “sourceImages” folder. Render and submit three 1024x1024 pixels high-quality images (PNG or JPG) and save them in the “images” subfolder. • All related files for this project must be organised correctly in a Maya project folder. Marking Criteria • Accuracy and detail of the 3D models. • Creativity and effectiveness of surreal arrangement and scaling. • Composition and use of camera angles to create visually appealing scenes. • Quality of shaders and lighting setup to enhance the overall presentation. • Organization of Maya file and folder structure for clarity and efficiency.
COMM1110 Evidence-Based Problem Solving Assessment 3: Case project part 1 Guideline and Marking Rubric Due date: Week 5, 11:59 am (Midday), Friday 21 March Project Overview Assume in February 2025, you are participating in a student internship program at Deloitte, a leading consulting and accounting firm in Australia. Your client, FreshGreen Grocery, a Sydney-based supermarket chain, is grappling with a significant issue: a sharp rise in food waste, particularly in its seafood and prepared meals sections. FreshGreen Grocery is keen to understand the root causes of this waste and is looking for actionable recommendations to address the problem. The store offers a variety of seafood, including fish and shrimps, as well as prepared meals like sandwiches, salads, and ready-to-eat dishes. Due to their perishable nature, these items have a relatively short shelf life. The increasing food waste is not only leading to financial losses but also raising environmental concerns for FreshGreen Grocery. Your primary task is to prepare an initial business analysis report on the rise in food waste within FreshGreen Grocery’s fresh food section. This report will serve as the basis for an internal discussion before presenting the findings and recommendations to FreshGreen Grocery. • Analytical: Identify potential factors contributing to food waste • Statistical: Analyse FreshGreen Grocery’s food waste data • Ethics: Assess the key ethical dilemmas • Word Limit: 1,500 words with a 10% buffer, allowing for a maximum of 1,650 words without penalty. There's no minimum word requirement, so any word count below 1,650 is acceptable. • Word Count Rule – everything in your report, such as headings, subheadings, and in-text citations, contribute to the word count, except for the reference list (bibliography), and any inserted screenshots or images. Use your Word document's built-in word count feature to check your word count accurately. • Structure and Format: No need to include an introduction or executive summary. Start your report directly with Section 1. Write in a formal style. (e.g., using an essay format, formal language, headings, and subheadings to make your report clear and easy to read). Ensure you include your full name and student ID in the footer of each page. • Referencing Style. While referencing is optional, if you choose to cite external information, adhere to the Harvard referencing style. for anything cited in your report. ( How to Cite Different Sources with Harvard Referencing | UNSW Australia) • We recommend that you use UNSW OneDrive to store your document online as a backup, in case you experience any technical issues with your computer. Section 1: Use Analytical Tools to Explore the Main Problem (40%) This section is approximately 600 words (guide only, not a word limit). 1) Define the Main Problem: Define the main problem concisely to provide clarity on the main issue requiring resolution for this assignment. Ensure that your report readers grasp a clear understanding of the background story and the primary focus of your report. 2) Scope the Problem: Based on what we've learned in lectures and tutorials, use the "5Ws Framework" to further analyse the problem. Select any two of the 'W's, and for each one, create three questions that explore different aspects of the issue. Then, identify the evidence needed to collect in order to answer each question. For each piece of evidence, specify the type of evidence required (e.g., Scientific Literature, Organizational Evidence, Practitioner Expertise, or Stakeholder Evidence). Instruction: Please use the table below to answer the question. All content within the 2W table will count toward the 1,500-word limit. Therefore, please integrate the 2W table directly into your report and present your response as text. 2ws Questions to Explore the Problem Evidence to collect Type of Evidence W.. 1. 1. 1. 2. 2. 2. 3. 3. 3. W.. 1. 1. 1. 2. 2. 2. 3. 3. 3. 3) Break Down the Problem: The "Logic Tree" is another tool commonly used to analyse problems. Based on what we've learned in lectures and tutorials, construct a logic tree to systematically break the problem down into its key sub-parts to pinpoint specific areas of concern and identify the key factors contributing to the food waste issue. Instruction: a) The logic tree needs to meet the Mutually Exclusive, Collectively Exhaustive (MECE) Requirements. You can create the logic tree using PowerPoint (instruction video available in Week 2's folder on Moodle) or any other tool you prefer. Ensure that all details in your logic tree are clearly visible. Marks may be deducted if your tutor cannot read the details due to blurriness. Please attach your logic tree as an image to your report. The logic tree image will NOT count towards the word limit. b) Prioritisation: Determine and justify which branches and/or sub-branches of your logic tree should be prioritized for further analysis. Provide a thorough explanation of your analytical process and clearly articulate the rationale behind your choices. This prioritization will serve as a guide for your data analysis in the subsequent stages. Section 2: Data and Statistical Analysis (40%): This section is approximately 600 words (guide only, not a word limit). Context: FreshGreen Grocery has identified that a substantial portion of the increase in food waste is due to an abundance of Seafood and Prepared meals reaching their expiration dates before being sold. FreshGreen Grocery has shared a dataset with you, concentrating on these two food items. A detailed description of the dataset is provided onpage6 and 7. Order Details: Each record in the Excel dataset represents a single order, comprising 200 pre- packed Seafood and Prepared meals. The dataset provides insights into the quantities wasted due to items remaining unsold before expiration date. FreshGreen Grocery Target: FreshGreen Grocery’s food waste allowance percentage for Seafood is capped at 7.5%, implying that in any given order of 200 pre-packed Seafood items, a maximum of 15 items should be wasted due to reaching expiration before being sold. The waste percentage for Prepared meals is capped at 12% (a maximum of 24 Items per order). FreshGreen Grocery's Operation: When orders arrive atthe FreshGreen Grocery store, they are initially placed in the storage area before being stocked on the shelves for sale. Operational protocols are implemented to optimize the availability of fresh products to customers while minimizing waste due to expiration. • For Seafood: Target is to have the items on the shelf for at least 7 days before the expiry date, given an expiration date of 12 days post-arrival at the storage area. • For Prepared meals: Target is to have the items on the shelf for at least 6 days before the expiry date, given an expiration date of 8 days post-arrival at the storage area. Data and Statistical Analysis Instructions: 1) Analyse Food Waste: a) Summary Statistics: Calculate the mean and standard deviation of food waste (variable "Quantity_Wasted") separately for Seafood and Prepared meals. Next, compare these results with FreshGreen Grocery’s food waste allowance target. b) Monthly Analysis: Based on what we learnt from our tutorials, creating PivotTable in Excel to conduct a separate monthly analysis of Seafood and Prepared meals waste, using the "Order_Arrival_Date" column to determine the order month. The primary objective is to illustrate the changes in Seafood and Prepared meals waste monthly. Choose suitable diagrams to present your analysis effectively in your report. 2) Investigate Shelf Lifespan FreshGreen Grocery suspects that logistic issues may be causing delays in moving items from storage to the shelves. This delay could reduce the display time of food items, contributing to increased food waste due to items reaching their expiration dates before being sold. Create a new variable: Create a new column called "Shelf_Lifespan" to calculate the number of days each order remains on the shelf before reaching its expiration date. Shelf_Lifespan = “Order_ Expiration_Date” – “Shelf_Stocking_Date” Conduct a monthly analysis of "Shelf_Lifespan" for both Seafood and Prepared Meals separately. Calculate the mean and standard deviation (SD) of "Shelf_Lifespan" for each month within each category. Focus on how these two key summary statistics vary on a month- to-month basis. You may use pivot tables or other relevant diagrams to visually represent your findings. Ensure that your report includes a clear summary of the main insights. If you encounter the "#VALUE!" issue while creating the new column,click here for solution 3) Analyse “Number of Orders” and “Prices” a) Use a pivot table to count the number of orders each month for Seafood and Prepared Meals separately. Provide a detailed explanation of your analysis and select appropriate diagrams to illustrate the monthly trends in the "total number of orders" for both Seafood and Prepared Meals separately. b) Analyse the monthly trends in "prices" for Seafood and Prepared Meals separately. Calculate the mean and standard deviation (SD) for each month within each category. Select appropriate diagrams to present your findings, and provide a detailed explanation of your analysis, focusing on how price trends may affect food waste. c) Let’s conduct a further analysis of the monthly trends in "prices" for Seafood and Prepared Meals separately. In addition to the standard deviation and average, select one or two other descriptive statistics (such as those discussed in our tutorials and lectures) for analysis. Choose appropriate diagrams to present your findings and provide a clear explanation of how these insights are relevant to addressing the issue of food waste. Section 3: Ethical Dilemmas with the Ethics Toolbox (20%) This section is approximately 300 words (guide only, not a word limit). FreshGreen Grocery is considering offering discounts on near-expiry products, encouraging both customers and local food banks to purchase them at a reduced price. While this strategy aims to minimize waste and promote affordable food access, some employees are concerned that it could damage the store’s reputation and erode customer trust. Others worry about the potential health risks of selling these products, even if they are still safe for consumption. Your task is to select one stakeholder impacted by FreshGreen Grocery's policy. Stakeholders may include FreshGreen Grocery, consumers, the local council, community organizations such as shelters, food banks, or product manufacturers. Identify one ethical dilemma faced by your chosen stakeholder in relation to this policy, assess the potential harm to individuals or entities (such as the environment, consumer safety, or local organizations), and elaborate on your reasoning, focusing on the ethical considerations of the identified dilemma within the context of FreshGreen Grocery’s food waste issue. Instruction: You are NOT required to apply the full “7-step Ethical Decision-making Framework”; your task is to identify one potential ethical dilemma from the perspective of your chosen stakeholder. You can consider using the 4 ethical lenses taught in our week 4 lecture and tutorials.
Econ 106D: Final Exam Problem 1: Third-Price Auction, Revisited (50 points) Consider a third-price auction with N ≥ 3 bidders. This is a sealed-bid auction in which the highest bidder wins and pays the third-highest bid. There is no reserve price. Assume that bidders’ values are independently and uniformly distributed on [0, 20] (i.e., ˜vi ∼iid U[0, 20] for all bidders i = 1, . . . , N). In Problem Set 4, we saw that this auction is not strategy-proof; in particular, it is not a dominant strategy for each bidder to bid his/her own value. In this problem, we explore this phenomenon in more detail. Please answer the following questions. (If you find it necessary, you may make the additional assumption that N = 3 and still receive most of the credit for this problem.) (a) (10 points) Provide a concrete example illustrating that truthful bidding is not a dominant strategy for bidder 1. (Hint: It may be useful to consider the argument that we used in class to show that the second-price auction is strategy-proof, and to see where that argument breaks down for the third-price auction. In particular, find a value v1 for bidder 1, a bid b1 ≠ v1, and a profile of bids (b2, . . . , bN ) for the other bidders such that bidder 1 does better by bidding b1 than by bidding v1.) (b) (20 points) Find a Nash equilibrium in which all bidders bid more than their true values. (Hint 1: Consider symmetric equilibria in which all bidders use strategies of the form. b(v) = βv, where β ≥ 0 is a constant you need to solve for. The overall approach to solving for this equilibrium is similar to that from class for the first-price auction.) (Hint 2: In the third-price auction—unlike in the first-price auction—bidder i does not know exactly how much he/she will pay conditional on winning. To compute bidder i’s expected payment conditional on winning, it may be useful to think about the second-order statistic among the remaining N − 1 bidders, i.e., the second highest value among all the bidders aside from i. It may also be helpful to recall that if x˜ ∼ U[0, b], then x˜ | [˜x ≤ c] ∼ U[0, c] for any c ≤ b. That is, for a uniform. random variable x˜, conditioning on the event that x˜ lies below a number c results in a new uniform. random variable on the “truncated support.”) (c) (10 points) Provide intuition for why bidders bid more than their true values in the above equilibrium. (Hint: It may be useful to recall the intuition for why bidders bid less than their true values in the first-price auction.) (d) (10 points) In class, we saw that the second-price auction admits a “bidding ring equilibrium” in which bidder 1 bids some number b1 ≥ 20 regardless of his/her true value, while the other bidders i = 2, . . . , N all bid bi = 0 regardless of their true values. Is this strategy profile also a Nash equilibrium of the third-price auction? Problem 2: First-Price Auction with Many Bidders (35 points) Consider a first-price auction with N ≥ 2 bidders. Assume that bidders’ values are independently and uniformly distributed on [0, 173] (i.e., ˜vi ∼iid U[0, 173] for all bidders i = 1, . . . , N). Please answer the following questions: (a) (15 points) Find the bidding strategies in a symmetric, linear Nash equilibrium. (That is, a Nash equilibrium in which all bidders use the same strategy b(v) = βv, where β > 0 is a constant you need to solve for.) (Hint: Mimic the two-bidder examples from class and Problem Set 4. Only one step of that derivation— determining how to write the probability that a given bid wins—requires real modification.) (b) (10 points) Describe how the bidding strategies you found in part (a) change as N, the number of bidders, (i) increases and (ii) tends to infinity (N → ∞)? Provide economic intuition for your answers. (c) (10 points) For fixed N, what is (i) the seller’s expected revenue and (ii) the expected bidder surplus? What happens to these objects as N → ∞? Provide economic intuition for your answers. Problem 3: SPA with Uncertain Number of Bidders (25 points, 10 bonus) In many real-world auction markets, neither the auctioneer nor the bidders know exactly how many bidders are participating in the auction. We can model this as follows. Let the number of bidders, N˜, be a random variable, and let pn = Pr(N˜ = n) denote the probability that there are n bidders. No one—neither the bidders nor the seller—knows the value of N˜. Each bidder i has a random value ˜vi ∼ U[0, 20] for the good. All of the bidders’ values, as well as the number of bidders, are independently distributed. Consider a second-price auction (with no reserve price) in this setting. Please answer the following questions: (a) (10 points) Find the bidding strategies in a symmetric, linear Nash equilibrium. (That is, a Nash equilibrium in which all bidders use the same strategy b(v) = βv, where β > 0 is a constant you need to solve for.) (Hint: Do not attempt to do this mechanically. Think carefully about which β is likely to work—the answer does not depend on the distribution of N˜, and can be determined with little calculation.) (b) (10 points) Consider two possible scenarios. In scenario (i), there is an equal chance of having 3 bidders and having 5 bidders (i.e., p3 = p5 = 1/2), so that the expected number of bidders is 4. In scenario (ii), there are 4 bidders for certain (i.e., p4 = 1). Does scenario (i) or scenario (ii) generate a higher expected revenue for the seller? (c) (5 points) Discuss implications of your answer to part (b) for the design of real-world auctions. (d) (Bonus: 10 points) Consider the following generalization of the setup from part (b). In scenario (i), N˜ has an arbitrary distribution with mean E[N˜] = µ, which is an integer. In scenario (ii), there are µ bidders for certain (i.e., pµ = 1). Does scenario (i) or scenario (ii) generate a higher expected revenue for the seller? Problem 4: School Choice (40 points) Consider a school choice market with four students {s1, ..., s4} and four schools {A, B, C, D}. Students’ preferences are as follows: s1 : A B C D s2 : C B D A s3 : B C D s4 : C B D A Schools’ priorities (which you can think of as having been set by the school district) are as follows: A : s1 > s2 > s3 > s4 B : s2 > s1 > s3 > s4 C : s3 > s1 > s2 > s4 D : s4 > s1 > s2 > s3 Each school has a single slot (“unit capacity”). Please answer the following questions: (a) (4 points) Consider the allocation {(s1, A),(s2, B),(s3, D),(s4, C)}. Is it Pareto efficient? Is it stable? How should we interpret stability here? Justify your answers. (b) (12 points) Suppose that the school seats are allocated via Serial Dictatorship in which students move in the order s3, s1, s4, s2. For each of the following, justify your answer: (i) What is the resulting assignment? (Assume truthful reporting for this part.) (ii) Is the assignment Pareto efficient? Is it stable? (iii) Find a Nash equilibrium of the preference-reporting game between students. (c) (12 points) Suppose now that school seats are allocated to students via the student-proposing DA algorithm. For each of the following, justify your answer: (i) What is the resulting assignment of school seats to students? (Assume truthful reporting for this part.) (ii) Is the assignment Pareto efficient? Is it stable? (iii) Find a Nash equilibrium of the preference-reporting game between students. (d) (12 points) Finally, suppose that school seats are allocated via the Boston Mechanism. For each of the following, justify your answer: (i) What is the resulting assignment? Describe the run of the algorithm. (Assume truthful reporting for this part.) (ii) Is the assignment Pareto efficient? Is it stable? (iii) Find two different Nash equilibria of the preference-reporting game between students that lead to different assignments (and provide an argument for why they are Nash equilibria).
ECON1272 Basic Econometrics Individual Assignment This is an individual assignment where you must work alone. You must submit an electronic copy of your assignment in Canvas in pdf, doc or docx format and your R code must be copied into Question 4. Hard copies will not be accepted. Show your calculations (if any) as well as answering the questions in clear full sentences. Log referrers to natural logarithm! Use the dataset: WDI_2404.RData Greenhouse gas emissions (GHG) per capita is an important contributor to global warming. The Paris Agreement (2015) foresees participating states to submit a Nationally Determined Contribution (NDC) to mitigate greenhouse gas emissions. You are a newly hired analyst tasked to model greenhouse gas emissions per capita worldwide. Assume that the outgoing research officer had started working on the econometric model to assess some of the drivers of GHG emissions. Now as an incoming research officer your job is to finish this research. Your variables of interest are: TGHG = Total greenhouse gas emissions (kt of CO2 equivalent) Pop = Population, total GDPpc= GDP per capita (constant 2015 US$) Mnf = Manufacturing, value added (% of GDP) REC = Renewable energy consumption (% of total final energy consumption) RIF = Renewable internal freshwater resources per capita (cubic meters) TNRR = Total natural resources rents (% of GDP) Trade = Trade as % of GDP LPI = Livestock production index Dependent variable: Greenhouse gas emissions per capita (GHGpc): We would like to estimate the relationship of other factors with this variable. It is defined as a fraction of Total greenhouse gas emissions over Total Population. Hint: Please create the level form. of your dependent variable in R, greenhouse gas emission per capita for each country first. Explanatory variables: GDP per capita (GDPpc): The richer a country is, some scholars expect higher greenhouse gas emissions per capita. Whether or not this relationship is linear or even reversible at high GDP per capita levels is hotly debated (see the concept of the Environmental Kuznets Curve). Manufacturing, value added as % of GDP (Mnf): A larger manufacturing sector of a country, is likely associated with more emissions. Renewable energy consumption in % of total final energy consumption (REC): When a country has a larger share of renewable energy consumption, we expect it to be associated with a lower cp. emissions of greenhouse gas per capita. Renewable internal freshwater resources per capita (RIF): Freshwater reserves are an indicator of hydro-electric potential, and thus a cleaner generation portfolio. Total natural resources rents in % of GDP (TNRR): High levels of natural resources are often associated with the concept of “resource lock-in”, or “fossil-fuel heavy pathways” . The higher the natural resource endowment and rent, according to this theory, the higher their expected usage in the economy. Trade as % of GDP (trade): Countries with higher levels of trade theoretically have better access to newer and cleaner technologies. Livestock production index: Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins (2014-2016 = 100). Higher levels of livestock are often associated with higher emissions (especially methane). All data originate from the World Bank (WDI). Please assess whether the above variables are truly associated with GHG emissions, and if yes, how. Answer the following questions: QUESTIONS: 1) Use R to create your dependent variable first, then run the following cross-sectional regression. (Please note the natural logs and construct these in R as needed): log(GHGPc) = β0 + β1 log(GDPPc) + β2 REC + β3 MNF + β4 log(RIF) + β5 TNRR + β6 LPI + u (Equation 1) a. Present your regression results in a table below (R output): 4 marks b. Interpret the constant (2.5 marks) and its p-value (1.5 marks). 4 marks c. Interpret the coefficient on GDP per capita and its p-value (1.5 marks each). 3 marks d. Interpret the coefficient on manufacturing value added and its p-value (1.5 marks each). 3 marks e. Interpret the coefficient on renewable energy consumption (as % of total energy consumption) and its p-value (1.5 marks each). 3 marks f. Interpret the coefficient on the livestock production index and calculate its t-stat. Interpret the calculated t-statistic (1.5 marks each). 3 marks g. Interpret the R2 of the regression. 2 marks h. Explain heteroscedasticity, its consequences (2 marks) and present the results of Equation (1) with heteroscedasticity robust standard errors (3 marks). Explain if any of your coefficient significance levels change (1 mark). 6 marks 2) Describe each of the Gauss-Markov assumptions and specify if they are likely to hold for the regression in Question 1 or not. 10 marks 3) Present a functioning R code reproducing the results. This is a critical part of the assignment without which we’ll initiate a plagiarism check. 2 marks Assignment Total: 40 marks
MECH 260-202: Midterm 2 (2023W2) Questions 1. (18 points) In the system below, discs B and D have a high-friction surfaces and do not slip relative to one another. If the applied torque Tp is 12N-m, what are the torsional reaction torques at A and C? 2. (7 points) Beam ABCD is supported by a pin joint at A and a roller joint at D. The magnitude of the distributed load between C and D begins at 1N/m at C and decreases linearly to ON/m at D. Draw a shear and bending moment diagram for the beam and mark the z location at which the maximum bending stress occurs (you do not need to calculate the exact value of z or the magnitude of the maximum bending moment). 3. A long slender beam is subjected to a varying bending moment as illustrated below: The beam is made of steel with Estcel=29000 ksi and has a W36x300 profile, which is horizontally and vertically symmetrical, has cross-sectional area A = 88.3in2 and height h = 36.74 in, and has second rectangular moments of area I=20300in and I,= 1110 in4. Given this information: (a) (4 points) Find most tensile and most compressive bending stress experienced anywhere in the beam. (b) (2 points) Mark the location (s) where the most tensile and most compressive bending stress appears in the "Side View" section of the diagram above or your own neat sketch. Be sure to label which is which. (c) (6 points) The beam is reinforced by adding a layer of aluminum with EAi = 10600ksi to its top surface. The final geometry is: Sketch one possible transformed cross-section which is relevant to analyzing the bending stresses which develop in the reinforced beam under the same loading M(z). Locate its the relevant neutral axis and evaluate the relevant second moment of area. Note: the flange and web thicknesses of the W36x300 profile are deliberately not provided.
CPT202 Software Engineering Group Project Coursework 1 - Software Requirements Analysis Start date 24th February 2025 Submission date 16th March 2025 Assignment type Individual assignment Percentage in final marks 20% Late submission policy 5% of the awarded marks shall be deducted for each working day after the submission date, up to a maximum of five working days. Late submissions for more than five working days will not be accepted. Important notes • Plagiarism results in the award of a ZERO mark; all submissions will be checked by TurnItIn. • The formal procedure for submitting coursework at XJTLU is strictly followed. Submission link on Learning Mall will be provided in due course. The submission timestamp on Learning Mall will be used to check late submissions. Learning Outcome • A. Work as part ofa development team demonstrating effective communication and interpersonal skills to design and develop a software system. • B. Demonstrate an understanding of the software development process including the principal methods and issues involved in deploying systems to meet business goals. • C. Specify the requirements ofa software system. • D. Understand the role of properly written documentation in the process of software development. Objective: To collaboratively analyze project requirements and independently develop well-defined Product Backlog Items (PBIs) using the Scrum framework. Coursework Process: 1. Group Discussion and Requirement Analysis: - Form groups to discuss and understand the project requirements thoroughly. - Collaboratively identify and outline the key modules ofthe project. 2. Module Assignment: - Within your group, assign each module to a different group member. – Ensure each member clearly understands their assigned module and its requirements. 3. Independent PBI Development: – Each student will independently develop PBIs for their assigned module. – Use the standard PBI format, including user stories and acceptance criteria, to ensure clarity and testability. 4. Peer Review Process: – Upon completion of PBI development, participate in a peer review process. – Evaluate a peer’s PBIs, providing constructive feedback focused on clarity, precision, and alignment with Scrum principles. 5. Final Report Submission: – Compile a final report that includes the following sections: Final Report Components: 1. Understanding of the Scrum framework – Provide a clear and concise explanation of the Scrum framework, emphasizing the role of PBIs in the process. – Strictly limit this section to 100 words. 2. Pre-Groomed PBI – Develop five pre-groomed PBIs relevant to the module assigned to you, each with a user story and acceptance criteria. – Ensure each PBI is well-defined and adheres to the PBI format. 3. Collaboration and Reflection – Peer Review and Feedback: • Document your participation in the peer review process. • Provide constructive feedback to peers, focusing on clarity, precision, and alignment with Scrum principles. • Feedback should be actionable, helping peers improve their work. • Evaluation criteria include constructiveness, depth of analysis, and clarity of communication. • Strictly limit this section to 250 words. – Reflection on Learning: • Detailed reflection on what you learned from the coursework and the peer review process. • Discuss insights gained, personal growth, and how feedback was incorporated. • Strictly limit this section to 100 words. Submission Guidelines: • Submit your final report as a single PDF document via Learning Mall by the above submission date, including all sections outlined in the Final Report Components above. • Ensure your PBIs are clearly formatted and easy to read. • DO NOT submit more than 5 PBIs. Marks will only be given to the first 5 PBIs in your report. • Include your peer review feedback and personal reflection as separate sections in the document. • Name the report as CPT202-A1-StudentID.pdf Marking Scheme 1. Understanding of Scrum and PBI Format (10 Marks) – Explanation of Scrum Framework (5 Marks): • Clear and concise explanation of the Scrum framework, focusing on the role of PBIs in the development process. – Correct Use of PBI Format (5 Marks): • Adherence to the standard PBI format, including user stories and acceptance criteria. 2. Quality of PBIs (20 Marks) – Clarity and Precision (10 Marks): • PBIs are clearly written and easily understandable by all stakeholders. – Logical and Relevance (10 Marks): • PBIs are logically relevant to the assigned module. 3. Pre-Groomed PBI (30 Marks) – PBI 1 (6 Marks): • Well-defined user story and acceptance criteria. – PBI 2 (6 Marks): • Well-defined user story and acceptance criteria. – PBI 3 (6 Marks): • Well-defined user story and acceptance criteria. – PBI 4 (6 Marks): • Well-defined user story and acceptance criteria. – PBI 5 (6 Marks): • Well-defined user story and acceptance criteria. 4. Collaboration and Reflection (40 Marks) – Peer Review and Feedback (25 Marks): • Students are required to participate in a peer review process, providing constructive feedback on another student’s PBIs. • Marks are awarded based on the quality, thoughtfulness, and specificity of the feedback provided. • Criteria for Evaluation: – Constructiveness (10 Marks): Feedback should be constructive, offering actionable suggestions for improvement. – Depth of Analysis (10 Marks): Feedback should demonstrate a deep understanding of the PBIs and Scrum principles. – Clarity and Communication (5 Marks): Feedback should be clearly communicated and well-structured. – Reflection on Learning (15 Marks): • Students must submit a detailed reflection (Approximately 100 words) on what they learned from the assignment and the peer review process. • Marks are awarded based on the depth of insight, personal learning, and application of Scrum principles articulated in the reflection. • Criteria for Evaluation: – Insightfulness (5 Marks): Reflection should provide deep insights into the learning process and understanding of Scrum. – Personal Growth (5 Marks): Reflection should highlight personal growth and how feedback was incorporated. – Feedback and Application (5 Marks): Reflection should demonstrate how feedback from peer review were applied or understood through the coursework.
Academic Year: 2024/25 Assessment Introduction: Course: BEng (Hons) Electronic Engineering Module Code: EL1242 Module Title: Digital Electronics Title of the Brief: Prototyping of A Primary Smart Home System Type of assessment: Coursework This Assessment Pack consists of a detailed assignment brief, guidance on what you need to prepare, and information on how class sessions support your ability to complete successfully. You’ll also find information on this page to guide you on how, where, and when to submit. If you need additional support, please make a note of the services detailed in this document. How, when, and where to submit: The deadline for this assessment is 21st March 2025 at 23.59 via the submission zone found the EL1242 Blackboard area - Please note that this is the final time you can submit – not the time to submit! If your work is submitted via the Turnitin link on Blackboard, the link will be visible to you on: 12th December 2024 Feedback will be provided by: 26th April 2025 You should aim to submit your assessment in advance of the deadline. Note: If you have any valid mitigating circumstances that mean you cannot meet an assessment submission deadline and you wish to request an extension, you will need to apply online, via MyUCLan with your evidence prior to the deadline. Further information on Mitigating Circumstances via this link. We wish you all success in completing your assessment. Read this guidance carefully, and any questions, please discuss with your Module Leader or module team. Additional Support available: All links are available through the online Student Hub 1. Academic support for this assessment will be provided by contacting Zengdi Bao or Xinliang Chen 2. Our Library resources link can be found in the library area of the Student Hub or via your subject librarian at [email protected]. 3. Support with your academic skills development (academic writing, critical thinking and referencing) is available through WISER on the Study Skills section of the Student Hub. 4. For help with Turnitin, see Blackboard and Turnitin Support on the Student Hub 5. If you have a disability, specific learning difficulty, long-term health or mental health condition, and not yet advised us, or would like to review your support, Inclusive Support can assist with reasonable adjustments and support. To find out more, you can visit the Inclusive Support page of the Student Hub. 6. For mental health and wellbeing support, please complete our online referral form, or email [email protected]. You can also call 01772 893020, attend a drop-in, or visit our UCLan Wellbeing Service Student Hub pages for more information. 7. For any other support query, please contact Student Support via [email protected]. 8. For consideration of Academic Integrity, please refer to detailed guidelines in our policy document . All assessed work should be genuinely your own work, and all resources fully cited. 9. For this assignment, you are not permitted to use any category of AI tools. Preparing for your assignment. Ensure that you fully understand the requirements for the assessment and what you are expected to complete. The assignment will be introduced in the lecture session where you can ask any questions, you can also ask for clarification by contacting the module team. The following module learning outcomes will be assessed in this assignment: · Explain the operation of a basic microprocessor system, relating the description to the architecture of the processor. · Interpret assembly language software used in the processor based system. Please read over the guide to writing a technical document https://www.theiet.org/media/5182/technical-report-writing.pdf and ensure that you fully understand the requirements of the assessment. There will be a lecture session on the assignment and writing a technical document. Ensure that you research and read into the subject area before writing the report so that you have a good background understanding to the subject area. Assignment Brief 1. Introduction In this assessment, the student will demonstrate the ability to: · Explain the operation of a basic microprocessor/microcontroller system, and interpret software used in such a system. · Write and test simple programs using a modern programming language and an appropriate set of development tools or environment, including use of libraries, debug tools etc. · Develop digital systems and programs for basic engineering applications. · Design hardware and software to meet the specification for a simple processor-based system. 2. Assignment Brief Smart home devices are a part of the larger concept of home automation. Large smart home systems utilize a main hub or controller to provide users with a central control for all of their devices. These devices can include lighting, heating and air conditioning and security system. The other key application of smart home is to provide assistance for disabled and elderly individuals. For example, they can be equipped with additional safety features which include sensors that monitor for medical emergencies such as falls or seizures. Smart home technology can provide users with more freedom and a higher quality of life. Your task is to implement a primary smart home system which can automatically control the heating and lighting system, detect people’s falls, and provide readings of environmental parameters, e.g. temperature, ambient light levels. You will use the Nucleo STM32L476RG ARM board and specially made sensor board, an application shield, shown in Figure 1. Figure 1. Nucleo STM32L476RG ARM board and the specially made sensor board. 3. Requirements Specific requirements of the systems are as follows: · Task 1: Automatic heating control. This is about a design of a thermostat. The state of the thermostat controller is represented by a servo. When the temperature is greater than or equal to 25oC, the servo should be at a position of 0o degree, which means the controller is ‘off’. When the temperature is below 25oC, the servo should be at a position of 135o degree, which means the thermostat controller is ‘on’. · Task 2: Automatic lighting control. Use the four red leds as lighting devices. There are five required levels of lighting, 1, 2, 3, 4 and off. The lighting level is controlled by the lightness/darkness detected by the LDR sensor, for example when it is very light, all the leds will be off, and when it is very dark all the four leds will be on. All the 5 levels of lighting will be corresponded to the evenly distributed ‘ReadIn’ value from the LDR. · Task 3: Detecting falls. Use the 3 axis (X, Y, Z) accelerometer to detect fall and the three-colour led to represent an alarm. Assume that when people fall, the value of acceleration is greater than 1.8g or less than-1.8g in any direction of X, Y and Z. When a fall happens, light up the three-colour leds in turn and each colour duration time is 0.2s. · Task 4: Environmental parameter readings. Use the blue User Button and switches to control the content of readings. When the blue user button is pressed, and the switch position is ‘0001’, display the temperature on the PC screen through the on board serial port. When the blue user button is pressed, and the switch position is ‘0010’, display the relative ambient light levels with row of ‘*’ on the PC screen (length determined by brightness). When the blue user button is pressed, and the switch position is ‘0100’, display the values of acceleration of 3 directions on the PC screen. When the blue user button is pressed, and the switch position is ‘1000’, display the output voltage value from the potentiometer. When the switch value is others, display a message of ‘invalid switch value’ on the PC screen. The summary of readings is as Table 1 below, State of Blue User Button Value of Switch Content to Display on PC Pressed 0001 Temperature ( oC ) Pressed 0010 Ambient light levels as a pattern of number of ‘*’ Pressed 0100 Values of acceleration of 3 directions Pressed 1000 Output voltage value from the potentiometer Pressed Other values ‘Invalid switch value’ Not pressed Do not care Keep previous display Table 1: Summary of environmental parameter readings 4. Details of devices on the specially designed shield. The details about the pin names, connections of devices are listed in Table 3. Table 2: Details of devices on the specially designed shield. 5. Development Report Part of your submission is a technical report about the development of the monitoring devices. Within the report ensure that you cover the following aspects: · Researched background into microcontollers. · Researched background into sensors, actuators and other components used in the tasks required. · Details of design analysis for all the tasks. · Individual testing on implemented tasks. · Appropriate images and references of the development. · Conclusions. Word limit: A maximum of 1000 words (see notes below for further information). Technical Report Writing To complete the report, you will have to thoroughly research the area using reliable sources and precisely reference where your information and statements are from. The aim of the report is to be clear, concise and convey technical information to the reader, note that the reader is familiar and experienced in the area. Ensure that you write your report for this audience. A guide on writing a technical document can be found at the following (this will also be uploaded to blackboard): https://www.theiet.org/media/5182/technical-report-writing.pdf Please read over the above document to ensure that you are clear on what a technical report is and know what you are required to complete, note the above is a guide not an explicit standard you will be required to ensure that your technical report contains the relevant information presented correctly for the reader. Ensure that you research and read into the subject area before writing the report so that you have a good background understanding to the subject area. You will need to provide a short report, which shows the calculation of each tasks in Marking Criteria and Weighting section below with an appropriate assumption, description and comments, no longer than 1,000 words. You should use the guideline below to structure your report. For the final reporting submission, make sure that each page is marked with the date of completion, the page number, and the total number of pages submitted. Make sure that the front page of your submission has this information displayed prominently along with the module name and number and assignment title. Your work must be referenced using Harvard Referencing system available here: https://v3.pebblepad.co.uk/v3portfolio/uclan/Asset/View/Gm3mmGk6sM3RgHZnjGfh7mm6pM. Further information to support your development will be available to view on assignment briefing session and Blackboard. Notes on Wordcount and Referencing For good marks and given the limited wordcount you should produce work that is: accurate; thorough; well-argued; clear; accurately referenced; relevant and written in correct (UK) English grammar and spelling. You may include figures and tables with short captions (25 words each) and a list of references without affecting the overall word count. Remember that you have limited words so ensure that you “stick to the point” and do not get into detail on superficial elements. Ensure that you include references when discussing technical facts and statements on the technology used. You must reference all your sources of information. These should be cited in the appropriate part of the report and fully identified to meet the Harvard referencing standard in a list at the end. Website articles must be properly referenced to be considered as legitimate references. Presentation of assignment work Except where specifically stated in the assignment brief, assignment work submissions should be word-processed, in Microsoft Word 2016 format, with a footer comprising: your module code; date; page number. The following module learning outcomes will be assessed in this assignment: · Explain the operation of a basic microprocessor system, relating the description to the architecture of the processor. · Interpret assembly language software used in the processor based system. Marking Criteria and Weighting It is recognised that not everyone will be able to meet all of the specifications given overleaf, so the assessment scheme is designed to measure how much of the specification you can meet. You will be required to write a short evaluative report and give a demonstration of your system by taking a 20 second selfie video. All the program with inline comments you made should be included in the report as an appendix (not part of page count). Marks will be awarded as follows in Table 2. Your submission will be marked in accordance with the following marking scheme: Item Possible Mark % Task 1: Automatic heating control 20 Task 2: Automatic lighting control 20 Task 3: Fall detection 20 Task 4:Environmental parameter readings 20 A 20 second video demonstrating your working system 20 Table 3: Summary of awarded marks.
Assignment: Risky Choice and CPT The Data You will be working with a dataset (DATA Study2 Rieskamp 2008.xls) from a paper by Rieskamp (2008). The dataset contains choices between pairs of gambles. The file on Moodle contains all gambles and choices, plus some demographic information. The sheet gambles summarises all unique choice pairs. The key is provided but most gamble pairs consists of lotteries with two outcomes. The Modelling Your objective will be to fit three different versions of Cumulative Prospect Theory (see Stott, 2006; Tversky & Kahneman, 1992) to the data. The stochastic choice rule should be the logit rule. All fitting must be done on the individual level. There are 30 participants in total (columns in sheet choices). You may still aggregate your model fitting results (e.g., model parameters, parameter recovery) to answer some of the questions below. Version 1: Fit the model with the α parameter for the curvature of the value function for both gains and losses. In addition include parameter λ for loss aversion: Version 2: Include an additional parameter β to capture the curvature of the value function for losses (in addition to α for gains). This model should also include λ for loss aversion: Version 3: In addition to the value function in Equation 2, you should also include the CPT probability weighting function as described in Tversky and Kahneman (1992). This function should not be used for cumulative probabilities/decision weights (i.e., you should apply it to the single event probabilities): Finally, the subjective valuation of a gamble A with outcomes xm > ... > x1 ≥ 0 > y1 > ... > yn with corresponding probabilities pm...p1 and q1...qn is: The logit function defining the probability of a gamble A being selected (compared to a gamble B) is: The Task Across your modelling attempts, you must illustrate the following tasks: 1. Estimate the parameter values for each model (i.e., model fitting). Summarise the results and comment on the findings. Do you find parameter values according to the literature (e.g., λ > 2, risk aversion/risk seeking)? What do the results suggest about participants’ risk preferences? Make sure that the model fits are not affected by local minima. 2. Model Comparison: Compare the 3 different models using appropriate methods and comment on the results. You should be able to answer questions such as “Is the subjective transformation of probabilities via the CPT probability weighting function a necessary component to explain risky choice?” and “Do we need separate parameters (i.e., α and β) to capture the subjective transformation of gains and losses?”. You should compare the model using the AIC and the likelihood ratio test (LHR), if models are nested. Do the two methods give you the same results? 3. Make scatter plots for each pairing of parameters, with dots representing each participant. You will see that some parameters are correlated over participants. For example, participants with low α have high bias/temperature parameter (τ in the logit rule), and vice versa. Why is this? What does it tell us about the psychological processes underlying risky choice? 4. Simulate data based on the initial model fits for each model (from a single set of starting parameter values, i.e., the best-fitting parameters for each model). Then, re-fit all three models to the simu- lated/generated datasets to assess whether the generating parameters can be recovered. Describe parameter recoverability across all three models. If recoverability is poor, explain why this might be the case. The Report Use R to implement the models and all appropriate analyses. Credit will be given for producing readable, annotated, simple, and clear code. Write the model as a function to which the choice attributes (amounts, probabilities) and model parameters are passed. The function should return choice probabilities. The examples from our seminars show how to do this for the standard EUT model. You can expand this code to capture different parameters of the CPT. Include the source code as an appendix (Section 2) in your report. Be sure to include comments in your code, explaining how the code relates to your description. The code should be runnable as a complete, stand alone script. That is, I should be able to copy and paste your code into R in one chunk and have it reproduce your analysis. The main body of your report, excluding the source code, should be fewer than 2,500 words. A good report might be much shorter. Plots supporting your arguments are highly recommended. Structure of the Report There should be two sections in your answer. In the first section, your answer should consist of “complete sentences”, as you would do in an essay. This section should start with a description of the design of the paper and the research questions, the different models used here (e.g., what are the free parameters used and what do they represent?), the benefits of using models, and relevant aspects of the data (e.g., how many participants, how many choices). Next, your answer should cover the main 4 Tasks of the assignment. The final part should contain a summary of the main findings of your analysis. Feel free to use headings to separate the parts in the first section in a reasonable manner. Include statistics in the text, or in tables or figures as appropriate. Tables and figures should be of publication quality (i.e., fully labelled). Given the correctness and appropriateness of your analyses, the first section will play the main role for your mark. If an analysis is performed in the second section, but not reported in the first, it will not be considered. Section 1 may be up to 2,500 words long (but can of course be shorter). Please note that too many figures or tables in this part can also reduce your mark. Section 2 should include the complete R code that you used and its output. You can add comments to explain what the code does. The code should show all of the commands that you used, enough for me to replicate exactly what you did (I will be copying and pasting code to run it, so make sure that works). You can include figures here that you used to explore the data that you do not wish to include in the first section. I will use the second section to help identify the source of any mistakes. For practical reports and papers you would only submit the first section, and thus the first section should stand alone without the second section. References Rieskamp, J. (2008). The probabilistic nature of preferential choice. Journal of Experimental Psychology: Learning, Memory, and Cognition , 34(6), 1446—1465. doi: 10.1037/a0013646 Stott, H. P. (2006). Cumulative prospect theory’s functional menagerie. Journal of Risk and Uncertainty , 32(2), 101—130. doi: 10.1007/s11166-006-8289-6 Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncer- tainty. Journal of Risk and Uncertainty , 5(4), 297—323. doi: 10.1007/BF00122574
IB2070 Mathematical Programming 2 Individual Assignment, 2024-25 (23-24) Assignment Instructions All assignments must be submitted ONLINE via my.wbs by 12pm (midday) UK time on the date displayed against this assessment. Please allow sufficient time to complete the automatically generated coversheet, especially if (where permitted) you have used a generative Artificial Intelligence (AI) tool during the process of completing your assessment as you will be asked to briefly outline which AI tool you have used and what you have used it for. Word Limit 1,500 words. Word Count Policy WBS has a school-wide policy on word counts. This is strictly enforced to ensure consistency across modules and programme. You can find more information about this policy in your Student Handbook under Academic Practice -7i. Word count policy. This is a strict limit not a guideline: any piece submitted with more words than the limit will result in the excess not being marked. Academic Practice Please ensure you read the full guidelines for Academic Practice in the Undergraduate Handbook and ensure you understand it. If in doubt, please seek clarification in advance of your submission. This includes important information on: • Cheating, plagiarism and collusion • Correct referencing • Using internet sources in assessments • Academic writing • English Language support • Word count policy When you submit this assignment online, you will be required to tick a declaration box indicating that the work involved is entirely your own. Each assignment will be put through plagiarism software to identify any collusion or inadequate referencing of materials used from different sources. Please do not submit images of your typed work unless you have been specifically requested to do so. We would consider taking action if your work: 1. is too reliant on the words of particular authors (rather than presenting your ideas in your own words), if the essay uses the ideas or words of an author without referencing them or putting their words into quotations (plagiarism). 2. suggests that you have worked very closely with another student or students (unless explicitly asked to do so by your Module Leader/Tutor) (collusion). 3. includes unreferenced work that you have previously submitted for any accredited course of study (unless explicitly asked to do so by your Module Leader/Tutor) (self-plagiarism).
158.326 Software Construction Tutorial 1 Create C# projects of Windows Forms App and Console App using Microsoft Visual Studio 2022 for the following scenario: A payment kiosk is used by staff and customers to pay for parking tickets. Parking rules are as follows: Staff: 1. The parking charges for the first ten hours are $2.00. 2. Further parking beyond 10 hours will incur a charge of $2.00 per hour. 3. Parking time cannot exceed 24 hours. If a car is parked for more than 24 hours, it will be towed away. Customer 1. The parking charges are based per hour. Parking per hour = $ 2.00 2. Parking time cannot exceed 24 hours. If a car is parked for more than 24 hours, it will be towed away. The program should display the parking charges for each staff or customer. Charges are calculated based on the number of hours a car has been parked. You have to make a two-tier design: Class Design Identify what classes you are going to use. What will be the fields, properties and methods? Hints: 1. Useful Math functions: Math.Ceiling, Math.Abs(x), Math.Floor(x), Math.Max(x,y), Math.Min(x,y) If a person has parked for 3.5 hours, the kiosk will charge him for 4 hours. So, use Math.Ceiling function Math.Ceiling(3.5) = 4 2. To display output in currency format – use ToString(“C”) (refer to http://msdn.microsoft.com/en-us/library/dwhawy9k(v=vs.110).aspx Reference: Pro C# 5.0 and .NET4.5 Framework Author: Andrew Troelsen ISBN: 978-1-4302-4234-5 Edition: 6th, Publisher: Apress, E-book Chapter 5 – Understanding Encapsulation Chapter 6 – Understanding Inheritance and Polymorphism Chapter 7 – Understanding Structured Exception Handling HINT: • User interface design and sample output for your Windows Forms App On selecting Customer or Staff, and clicking the “Next” button: The parking amount varies for customer and staff If parking exceeds 24 hours, an exception is thrown from a class member and caught by the form. • Sample output for your Console App:
CCC8015 Generative Artificial Intelligence Individual Assignment Due: 23:59 Friday 28 Mar 2025 Important notes ● Point Allocation: This assignment carries a total of 100 points, which will be awarded based on the criteria outlined in the grading rubric. It accounts for 14% of your final score. ● Use of Visual Aids: You are encouraged to enhance your report with diagrams or charts where appropriate. Visual aids should be used to complement and clarify the concepts discussed in your text. ● Citation Requirement: If you refer to or incorporate external information, proper citation is mandatory. This includes any direct quotes, paraphrased information, or data and statistics you include in your report. ● Generative AI Use: The use of generative AI tools is permitted and even encouraged to assist with the creation of your report. However, it is imperative that any content generated by AI is clearly indicated as such in your submission. This transparency is necessary to maintain academic honesty and will be taken into consideration during grading. ● Plagiarism Policy: Originality in your work is crucial. Plagiarism, which includes copying someone else's work without credit, submitting someone else's work as your own, or using generative AI tools to create content without disclosure, will lead to a failing grade for this assignment. ● Similarity Report: The acceptable similarity threshold is 25%. Assignments surpassing this threshold may be flagged for plagiarism. However, if the system primarily detects quotes from questions and references, such matches should be disregarded. We specifically evaluate the similarity of the 'content' to ensure it falls below the 25% threshold. ● Grading Rubric: Assignments will be evaluated based on a clear demonstration of subject mastery, critical thinking and originality, logical organization, writing quality, effective use of visual aids and references, and adherence to assignment guidelines. ● Concerning late submissions for assignments in CCC8015, we are implementing a segmented deduction scheme. For assignments, there will be a 10% deduction for submissions within 3 days, 30% for submissions within a week, and no points awarded for submissions exceeding one week. ● For students with special educational needs (SEN), there will be a one-week extension of the submission deadline (with an additional 20% time allowance). Deliverable Assignment Deliverable: Submission Guidelines: (Report + Google Colab PDF document) 1. Your report should include tables, figures, and a reference list. 2. A title page is not required. 3. Submit a Google Colab PDF document demonstrating your code implementation and data visualizations. When preparing your assignment, please ensure the following: 1. Answer all the questions 2. The total word count should not exceed 1500 words, excluding the questions, appendix, and references. 3. The 1500-word limit should be distributed across all the questions as needed. There is no requirement for an equal allocation of words to each question. 4. Upload your assignment in either PDF or Word format. Other file formats are not permitted. 5. When submitting your assignment via Turnitin, download the document first and then upload the downloaded file. This will prevent any issues with accessing your assignment for grading. 6. Do not include the questions in your assignment to avoid potential high similarity scores in Turnitin. 7. You are encouraged to use ChatGPT to assist you in completing your assignment. 8. Formatting guidelines: 。 Font: Times New Roman 。 Size: 12 。 Color: Black 。 Spacing: No specific requirements Background This assignment aims to leverage generative AI for data analysis. Imagine you are a novice in the field of data science, but you have diligently recorded your daily expenses over the past two years. As a result, you have amassed a dataset detailing your daily expenditures. Your task is to utilize generative AI for in-depth data analysis, including visualization, insight discovery, and to make recommendations for enhanced future financial planning based on your expenditure patterns over the next year. In your assignment, you should: ● Analyzing and presenting data in your Google Colab using Python. Communicating your findings and interpretations through written explanations and graphs. Based on your findings, you will be expected to provide recommendations. Show these graphs and findings in the Word documents. Data Dataset name: ‘Family_expenditure_dataset_CCC8015.csv’ Definition of each columns 1. Rental Expense: Expenditure related to renting an apartment, with payments due on the last day of each month. 2. Transportation: Includes daily transportation expenses as well as long-distance transportation costs. 3. Food: Expenses encompassing payments for restaurants or any food consumption. 4. Water Expense: Costs associated with water usage in the apartment. 5. Electric Expense: Expenditure on electricity usage within the apartment. 6. Clothing: Expenses for purchasing clothes, occurring in specific months only. 7. Entertainment: Costs related to travel or entertainment activities such as movies or theme parks. 8. Sport: Expenses for exercise-related activities, including fees for courts and equipment. 9. Investment: Allocation of funds for family investments on a monthly basis. Instructions for loading the CSV to Google Colab: Step 1: Download the dataset 'Family_expenditure_dataset_CCC8015.csv'. Step 2: Click on the 'Files' icon and then upload the dataset to the files section. Step 3: Execute the code in the code cells. import pandas as pd # Load the CSV file into a Pandas DataFrame. df = pd.read_csv('Family_expenditure_dataset_CCC8015.csv') # Display the first few rows of the DataFrame. df Question 1: [25 Marks] Task 1: Create a pie chart to visualize the proportion of expenses. Generate the code and successfully run it in Google Colab. Display the graph in your Google Colab. [15 Marks] Task 2: Include the generated pie chart in the report and analyze the data. Offer interpretations or insights. [10 Marks] Question 2: [25 Marks] Task 1: Conduct a monthly time series analysis (line chart) of the 'Water Expense' column. Generate the code and successfully run it in Google Colab. Display the graph in your Google Colab. [15 Marks] Task 2: Include the time series graph in the report and analyze the data trends. Offer interpretations or insights. [10 Marks] Question 3: [25 Marks] Task 1: Conduct a monthly time series analysis (line chart) of the 'Electric Expense' column. Generate the code and successfully run it in Google Colab. Display the graph in your Google Colab. [15 Marks] Task 2: Include the time series graph in the report and analyze the data trends. Offer interpretations or insights. [10 Marks] Hints: When using ChatGPT to generate code (assuming you have already imported CSV into your Google Colab), 1. AI may assist you in inputting your data (refer to steps 2 and 3). Exclude the code from steps 2 and 3 when running it. 2. The code from steps 4 and 5 should only be included once in Question 2. Remove the code if it is generated again by AI for other questions. Task 1: Ask ChatGPT to explain the following code and answer the following questions: 4.1: Explain the code snippet provided below. In this question, you should clarify what the input and output are. [10 Marks] 4.2: What is the purpose of the first three lines of the code? Why do we need to set these libraries with shorter terms, for example, pandas as pd? Is the code executable if the first three lines are not included? [15 Marks] ***The green words marked with # in the code represent comments for human readability. These comments are intended to provide hints for answering questions and are not meant for assessing the dataset. Please note that you are not required to include code to load or assess the dataset in this question.*** import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Load the dataset # Assuming 'df' contains your dataset with columns 'Transportation' and 'Entertainment' # Create a scatter plot to visualize the correlation plt.figure(figsize=(8, 6)) sns.scatterplot(x='Transportation', y='Entertainment', data=df) plt.title('Scatter Plot of Transportation vs Entertainment') plt.xlabel('Transportation') plt.ylabel('Entertainment') plt.show()
CEM200: Human Resources Management and Real Estate Development and Appraisal - Real Estate Development and Appraisal Section Assignment Details Assignment Name: Real Estate Valuation Assignment Weight: 100% of the final mark Word Limit: 1000 words ± 10% for the essay – No limitations for the Excel file Due Date: 12:00 25/03/2025 Assessment & Feedback date: 15 Uni working days Assignment Brief READ THIS BRIEF SEVERAL TIMES TO ENSURE NOTHING IS MISSED. IF YOU BELIEVE SOMETHING SHOULD HAVE BEEN INCLUDED, MAKE REASONABLE ASSUMPTIONS AND EXPLAIN THEM IN YOUR SUBMISSION. THIS EXPLANATION WILL NOT COUNT TOWARD YOUR WORD LIMIT. Scenario A developer has been offered two sites. Both sites are in similar locations and are similar projects. Both sites are believed to have low risk of geo-technical issues. Site/Project A is on offer for £5.5 million. It has planning consent for a low-density development of 100 residential units that are 85 sqm Net Sellable Area each. 40% of the units are affordable housing. 20% will be social rented and are expected to sell at 50% of the Market Value of private dwellings. 20% will be intermediate tenure and are expected to sell at 80% of the Market Value of private dwellings. Affordable housing will be sold and paid for in the same pattern as private housing in terms of timing. Site/Project B is on offer for £3.5 million. It has planning consent for a high density development of 100 residential units that are 85 sqm Net Sellable Area each. It is a single six storey building. It has the same affordable housing contribution in terms of the units. Under a ‘Golden Brick’ agreement, it is expected that 50% of the revenues from affordable housing will be received at commencement of demolition and 50% will be received at Practical Completion. Unless a distinction is made, you should make the same assumptions for Site/Project A and Site/Project B. Sale prices: It is estimated that private housing will sell at £5,000 psm of Net Sellable Area. For Site/Project A, 10 units are expected to be sold per quarter following the commencement of base build of dwellings. For Site/Project B, all units are expected to be sold during construction with all revenues from private units from contracted sales received at Practical Completion. It is expected that sale prices will increase at 1% per quarter from 1 October 2025. Base build costs: It is estimated that base build costs for the low-density units will be £2,250 psm of Net Sellable Area in October 2025. It is estimated that base build costs for the high density apartments units will be £2,500 psm of Gross Internal Area in October 2025. The Gross Internal Area of Project B is 10,000 square metres. It is expected that base build costs for Site A will rise at 2.5% per quarter in the period from 1 October 2025. For Site/Project A, 10 units are expected to be completed per quarter following the commencement of base build with costs of base build payable at the end of the quarter. For Site/Project B, for the single building, the following cost distribution should be assumed: Base Build Quarter 1, 5% Base Build Quarter 2, 5% Base Build Quarter 3, 10% Base Build Quarter 4, 10% Base Build Quarter 5, 20% Base Build Quarter 6, 20% Base Build Quarter 7, 10% Base Build Quarter 8, 10% Base Build Quarter 9, 5% Base Build Quarter 10, 5% It is anticipated that a fixed price contract will be the procurement route. Construction prices are expected to increase by 15% from current levels until the commencement of base build. Developer,s contingency: 5% of base build costs. This should be incorporated in the cash flow on a quarterly basis and linked to base build costs in each quarter. External works: For both sites, it has been estimated that external works will be £5,000 per unit. For Site/Project A, the costs of external works will be phased in line with construction of the units. For Site/Project B, it is expected that the costs of external works will be spread equally over the last two quarters ofthe construction period. For both projects, the following assumptions should be made: Professional fees: It is estimated that professional fees will be 6% of base build costs. It is estimated that 50% of the professional fees will be incurred in the mobilisation phase spread equally over the mobilisation period with the remaining 50% spread equally over the demolition and base build periods. Demolition and site preparation: These costs are estimated at £1,000,000. All of these costs are expected to be incurred in the quarter prior to commencement of construction. S106 and CIL costs: It is expected that S106 and CIL costs will be £10,000 per unit. These are payable in a single sum at the commencement of demolition and site preparation. Sales and marketing fees: 1% of sale price Purchasers, fees for land: 6.8% of land price. Land acquisition: If the bid is accepted, it is expected that the land will be acquired on 1 October 2025. Following land acquisition, it is expected that there will be a nine month mobilisation period. Demolition and site preparation works are expected to start on 1 July 2026. Base build is expected to start on 1 October 2026 with Practical Completion at 31 March 2029. You have been asked to provide two cash flow models of the two projects incorporating financial performance metrics for each project. For each project, the cash flow models should be used to estimate: √ IRR √ Cash profit √ Equity multiple √ Profit on cost % √ Profit on GDV % Drawing upon the module content, related academic literature and professional guidance where appropriate, you then should critically compare and evaluate the expected financial performance of two projects with reference to differences in their relative risks and attributes of the projects and the resultant cash flow patterns. This should be in essay format and include a bibliography based on Harvard style referencing Format of submission . You must do all the calculations in MS Excel, using cell referencing to your initial data, which has been provided in the assignment brief. You must submit your Excel file. . You may use your Excel tables in your essay as well, to demonstrate your interpretations. You may even embed your Excel file tables in your MS-Word document. . You must submit the 1000-word essay in PDF format. . This is an academic writing, so please follow university’s academic writing guidelines . You have to have two cash flow models submitted. Make sure they are clearly recognizable in your Excel file.
